Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Jiegeng decoction is a classic prescription composed of two Chinese medicinal herbs： Platycodon grandiflorum and Glycyrrhiza uralensis. It has the efficacy of diffusing lung qi， resolving phlegm， relieving sore throat and discharging pus， and is commonly used in the treatment of respiratory diseases such as cough and pharyngodynia. This article reviews the chemical components， pharmacological mechanisms and clinical applications of Jiegeng decoction. It was found that Jiegeng decoction contains triterpenoid saponins， flavonoids， glycosides， acids， and other components， with platycodin D， platycodin D2， glycyrrhizic acid， glycyrrhetinic acid， liquiritin， etc.， serving as the main active pharmaceutical ingredients. Jiegeng decoction and its chemical constituents exert anti-inflammatory effects by inhibiting signaling pathways such as nuclear factor-κB and mitogen- activated protein kinases， and demonstrate anti-tumor activities through mechanisms like modulating the tumor immune microenvironment and promoting cancer cell apoptosis. Additionally， it exhibits various pharmacological actions including antibacterial， antiviral， and antioxidant effects. Clinically， Jiegeng decoction， its modified prescription and compound combinations are widely used in the treatment of respiratory diseases such as cough， pneumonia， and pharyngitis， as well as digestive system disorders like constipation.

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Objective To summarize and evaluate the characteristics of current recurrent spontaneous abortion(RSA)animal models at home and abroad,and to provide reference and guidance for the standardized preparation of RSA models.Methods"Recurrent spontaneous abortion"and"animal model"were used as co-keywords in CNKI,Wanfang,VIP,PubMed and Web of Science databases to search the RSA animal experimental literature,covering the period up to January 20,2024,and a total of 1 411 articles were collected.The analysis focused on construction methods and essential elements of RSA animal models,the modeling process and result evaluation,as well as the application of these models in pharmacological and pharmacodynamic research.An Excel table was established for systematic analysis and discussion.Results A total of 138 experimental studies were obtained after screening.In constructing RSA animal models,immunological models were the most widely used in Western medicine(96.92％),with the Clark model being the main one(92.31％).In traditional Chinese medicine(TCM)models,70.00％were kidney deficiency-luteal inhibition-syndrome combination models,20.00％were kidney deficiency and blood stasis models,and 10.00％were deficiency-heat syndrome models.Most animals were selected at 6-8 weeks(33.86％)and 8 weeks(32.28％)of age.The majority of animals were paired for mating at 18:00 on the day of cage pairing.In 81.03％of literatures,vaginal plugs were checked once the following morning,with 8:00 being the most common time(17.02％).The most commonly used drug administration cycle was 14 days of continuous gavage after pregnancy.Among the tested drugs,Western drugs were mainly protein-based(29.17％),while TCM drugs were mainly TCM decoction(81.11％).The most frequently used methods for detecting indicators included visual observation of embryos(22.54％),western blot(15.96％),PCR(13.58％),ELISA(12.91％),HE staining(10.80％)and immunohistochemistry(9.39％).Conclusion The etiology of RSA is complex,and corresponding animal models should be established based on different etiologies.Clark model is commonly used in the construction of Western medicine model,while the kidney deficiency-luteal inhibition-syndrome combination model is predominant in TCM.RSA animal model is widely used in related research,but systematic evaluation needs to be strengthened.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective To develop a biological safety protection third-level(BSL-3)laboratory based on folding-modular shelters to solve the problems of the existing laboratories in space and function expansion,large-scale deployment and low-cost transportation.Methods The BSL-3 laboratory was composed of a folding combined shelter module,a ventilation and purification module,a power supply and distribution module,a monitoring and communication module,a control system module and an equipment module.The folding combined shelter module used a leveling base frame as the foundation and a lightweight panel as the enclosure mechanism,and was divided into an auxiliary area and a protection protected area;the ventilation and purification module was made up of an air supply unit and an air exhaust unit,the air supply unit was integrated with a fresh-air air conditioner and the exhaust unit was equipped with a main fan,a standby fan and a bag in/bag out filter;the control system module adopted a supervision mode of decentralized control and centralized management,which executed communication with the data server as the center and Profinet protocol and MODBUS-TCP.Results The BSL-3 laboratory proved to meet the requirements of relevant standards in internal microenvironment,airflow direction,airtightness,working condition and disinfection effect.Conclusion The BSL-3 laboratory is compatible with large-scale transport and deployment and facilitates reliable and safe experiments for epidemic prevention and control and cross-regional support.[Chinese Medical Equipment Journal,2024,45(3):41-46]

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To test the reliability and validity of the general procrastination scale (GPS) in the application of middle school students.Methods:The Chinese version of GPS, the irrational procrastination scale(IPS), and the Maslach burnout inventory(MBI) were utilized to survey 10 825 middle school students in Harbin City through stratified random sampling, and 4 498 students were retested after 4 weeks. Statistical analysis was performed using SPSS 27.0 and Mplus 8.0.Results:The entries were well differentiated.Exploratory and confirmatory factor analysis indicated that GPS was composed of two factors, including active avoidance and lack of planning.The model fit was good (CFI=0.914, TLI=0.901, RMSEA=0.069, SRMR=0.072). GPS was positively correlated with the total scores of IPS and MBI ( r=0.753, 0.677, both P<0.001). The Cronbach's α coefficient of GPS was 0.864, the folded half reliability was 0.870, and the retest reliability after 4 weeks was 0.756. Conclusion:The GPS has good reliability and validity among middle school students, which provides a standard for measuring the procrastination level of middle school students and carrying out related research.

Objective To evaluate the CT features and differential value of gastric leiomyomas(GLs)and gastric stromal tumors(GSTs)after propensity score matching.Methods Twenty-six GLs were 1∶1 propensity score matched to GSTs based on sex,age,tumor site and size.Tumor shape and contour,mucosal ulcer,tumor growth patterns,enhancement pattern and degree,the lon-gest diameter(LD)of the tumor,and the ratio of the LD to the vertical diameter(VD)were analyzed.CT signs included hemor-rhage,calcification,peripheral invasion,and distant metastasis,etc.Regression analysis was used to determine the best quantitative evaluation for differentiation of them.Results The presence of mucosal ulcer was significantly more frequent in GSTs than in GLs(P=0.032).Both tumors showed progressive enhancement;however,the enhancement degree of GSTs was significantly higher than GLs in the arterial and portal venous phases(P=0.004,P=0.002,respectively).The above influential factors were included in a regression analysis using enhancement degree of 18 HU and 23 HU in the arterial and portal venous phases as cutoff values respectively.An enhancement degree≤18 HU in the arterial phase was identified as an independent influential factor in the diagnosis of GLs[odds ratio(OR)=12.776,95％confidence interval(CI)1.270-128.535].No significant difference was found in other morphological characteristics(P＞0.05).Conclusion Less ulceration on the tumor surface and mild enhancement in arterial phase are characteristic features of GLs compared with GSTs.